How Web Scraping Api Is Used To Extract Real Estate Website Data?

By Author: Web Scraping API is USed To Extract Real Estate
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In this blog, we will discuss about scraping real estate data using Python and creating dataset as per the requirement. For example, here we will extract real estate information of New York City. The website's information is not permanently contained inside the HTML code but is dynamically produced by issuing a POST request to Realtor.com's API. The reply to the request will then returns the data from the website in the form of JSON formatted strings. As a result, your goal is to "fake" the POST request in your Python script, claiming to be the Website requesting data from the API.

When you click on the “Response” tab, you will check that the reply to the request will be data extracted from the real estate listings viewed on the webpage.

To define what data you want to get, you must submit a request payload with POST requests. When you click on the "Payload" tab, you will discover the Request Payload. You must also click on "see source" if you want the request payload to be a string rather than a json dictionary. Within this question packet, you will specify the data you want to retrieve, the results ...
... of the second page of the Real Estate Listings website for the city of New York.

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